The Collaborative Optimization Between Active And Passive Heating Technologies For Residential Building Carbon Emissions Reduction In Tibet

The booming economy in Tibet has led to higher requirements for indoor comfort and higher heating needs.Higher heating needs means more heating energy consumption and increasing carbon emissions of buildings.Higher carbon emissions exacerbate the increasingly severe greenhouse effect,and then damage the environment and induce natural disasters.The ecological environment in Tibet is fragile,and the negative impact of the greenhouse effect will be more severe in Tibet.Moreover,in Tibet,tourism is one of the major industries,so environmental damage means economic loss.Globally,the carbon emissions of the construction industry currently account for nearly one-third of the global total carbon emissions.The construction industry has become one of the main sources of carbon emissions.Reducing building carbon emissions through building optimization design has become one of the current research hotspots.However,in the current architectural design process in Tibet,the building's active and passive heating design were designed in sequential,which means the building passive design and active design is separated.The separation of active and passive design means incoordination between active and passive measures,and the full performance of passive or active heating measures cannot be fully utilized,and the total carbon emissions of buildings cannot be reduced effectively.Therefore,there is still a lot of room for carbon emissions reduction of buildings in Tibet.Through the collaborative optimization between active and passive heating technologies to minimize building carbon emissions,it can mitigate the impact of economic development on the environment.In summary,this article will study the collaborative optimization between active and passive heating technologies to minimize carbon emissions of buildings in Tibet.In this research,building optimization is based on existing building simulation and optimization software.It uses Open Studio,Energy Plus,and Design Builder for building modelling,and uses jEPlus,jEPlus+EA for building active and passive heating measures collaborative optimization.Based on this method,an auxiliary software for building active and passive heating measures collaborative optimization has been developed.Then this method has been applied to collaborative optimization of active and passive heating measures of building in Tibet.The optimization algorithm used in this method is the NSGA-II multi-objective optimization genetic algorithm.The optimized passive heating measures'variables are north-south wall insulation thickness,window type,window-to-wall ratio,building orientation,and depth of sunspace.The optimized active heating measures are traditional boilers system,solar plus boiler system,solar plus electric auxiliary heating system,solar plus air source heat pump system,solar plus ground source heat pump system.For the optimization of a solar system,there is one more parameter called collector area.The optimization targets are set to the building embodied carbon emissions and the operational carbon emissions.The optimized Pareto front gives the relationship between these two optimization goals,and then the total of the building carbon emissions is set as the decision conditions to select the optimal solution.The main conclusion of this study is as follows:(1)Based on the existing building simulation and optimization software,the active and passive collaborative optimization process has been integrated to make it easier to operate,and the auxiliary software for active and passive collaborative optimization of the building is developed to further reduce the difficulty of operation.(2)Using the active and passive collaborative optimization method of buildings,with the purpose of minimizing building carbon emissions,the active and passive heating measures of typical buildings in typical areas of Tibet are collaboratively optimized.Given the use of traditional boilers,solar collector plus boilers,solar collector plus electric heating,solar collector plus air source heat pumps and solar collector plus ground source heat pump systems,the optimal solution of building orientation,differential insulation of the envelope structure,the depth of the sunlight and the area of the heat collector are given.The results show that active and passive collaborative optimization significantly reduces building carbon emissions.(3)For the collaborative optimization between passive measures and boiler systems or solar collector plus auxiliary heating,when the genetic algorithm population and the maximum generation are set to 24 and 200,the optimal solution of the single factor test analysis is consistent with the optimal solution optimized by the genetic algorithm,and the optimization results fully converge.(4)The coupling relationship between different active systems and passive measures is different.The more efficient the active system,the lower the insulation level of the building.So,the active system takes more building heating loads to fully exerts the performance of itself and reduces the carbon emissions of the building as a whole.While collaborative optimization reduces building carbon emissions and significantly reduces building energy consumption.For the solar energy plus auxiliary heating system,the building energy consumption is less than 1 kWh/(m~2·a),which is close to zero energy consumption.(5)For the solar energy plus auxiliary heating systems,the solar system takes almost the entire building heating loads.And,the more efficient and low-carbon the auxiliary heating system is,the more heating loads it takes and the lower the solar fraction.When the auxiliary heating system is air source heat pump and ground source heat pump,the solar fraction of the system is about 95%and 90%,respectively.(6)For the passive plus boiler system,the optimization result of the heat transfer coefficient of the envelope mostly is lower than code;for the passive plus solar collector and auxiliary heat source system,the heat transfer coefficient mostly is higher than code.To use active and passive collaborative optimization methods to design buildings can more effectively reduce the total carbon emissions of buildings than to design buildings according to code.